Determining Mobile Video Quality of Experience and Impact of Video Transcoding

ABSTRACT

Video data packets transmitted through a wireless network are captured by a network monitoring system. Video data sessions are detected from the video data packets. Key parameters are identified within the video data packets, such as video bit rate, resent or failed video packets, and video session duration. A Quality of Experience (QoE) is determined for some or all users associated with the video sessions based upon the key parameters. A header extension is added to the video data packets by a transcoding system. The header extension includes data associated with original and transcoded video data packets. The network monitoring system monitors the header extension and evaluates the effect of video transcoding upon the overall QoE for users. The monitoring system provides feedback to a transcoding policy engine based upon the effect of transcoding upon QoE.

TECHNICAL FIELD

Embodiments are directed, in general, to monitoring video datatransmission in mobile networks, and, more specifically, to evaluatingthe effect of video data transcoding on user Quality of Experience(QoE).

BACKGROUND

As mobile data networks continue to experience an unprecedentedexplosion in total network traffic, mobile devices consume large amountsof wireless network bandwidth. The increase in network traffic islargely driven by web-enabled smart phones and mobile-connected laptopcomputers. Within the overall network-growth trend, mobile video isexpected to become the dominant consumer of mobile-data bandwidth.

With bandwidth demand exploding in mobile networks, service providersmust expand their radio networks to keep up with data growth. However,adding radio transmitters to keep up with bandwidth growth is not alwayspossible or economical. Building out the mobile networks to supportthese traffic volumes is expensive. All data ultimately originates orterminates at the user equipment, which requires transmission of thevideo data over scarce radio resources.

SUMMARY

Embodiments of a network monitoring system are directed to evaluating auser Quality of Experience (QoE) in a wireless network. The networkmonitoring system captures data packets from network interfaces in thewireless network and identifies video sessions associated with thecaptured data packets. The network monitoring system monitors keyparameters for the video sessions within the captured data packets anddetermines an effective throughput per video session based upon the keyparameters. The network monitoring system further calculates a QoE scorefor one or more users associated with the video sessions. The keyparameters may comprise one or more of a video bit rate, a number ofresent video data packets, a number of failed video data packets, and avideo-session duration.

The network monitoring system may determine a video bit rate for each ofthe video sessions and then calculate the QoE score based at least inpart upon the video bit rate for one or more video sessions.Alternatively or additionally, the network monitoring system may detectoccurrences of resent or failed video data packets and calculate the QoEscore based at least in part upon a number of resent or failed videodata packets, and/or determine a duration of a selected video sessionand calculate the QoE score based at least in part upon the duration ofthe selected video session.

A video transcoding device may insert a header extension comprisingsummary data into transcoded video data packets associated with thevideo sessions. The network monitoring system further evaluates animpact of video transcoding on the QoE and sends updates to atranscoding policy engine. The updates identify the impact of videotranscoding on user QoE.

In another embodiment, a system for evaluating user QoE in a wirelessnetwork comprises a plurality of monitoring probes coupled to one ormore network interfaces. The monitoring probes are adapted to capturedata from the network interfaces. A processor is adapted to analyze thedata captured from the network interfaces. The processor identifiesvideo data within the data captured from the network interfaces andidentifies one or more video sessions associated with the video data.The processor further determines an effective throughput for the one ormore video sessions; and determines a QoE score for one or more usersassociated with the video sessions. The QoE score is based upon theeffective throughput of the one or more video sessions.

The processor may be part of a network monitoring system and may furtheridentify key parameters within the video data, determine video bit ratesfor the one or more video sessions, detect resent or failed video datapackets associated with the one or more video sessions, and/or detectdurations of the one or more video sessions. The processor thendetermines the effective throughput for the video sessions based atleast in part upon the key parameters, the video bit rates, the resentor failed video data packets, and/or the durations of the videosessions.

Transactional analysis can be used to determine video QoE. In oneembodiment, a QoE evaluation process running on the network monitoringsystem maintains a record of video transactions for some or all users aswell as a transactional record of any video delivery. The QoE evaluationsystem identifies the following scenarios and characterizes theassociated session as having a low or poor QoE:

video data that is delivered at less than a target bit rate;

video data sessions that are aborted after short playback;

multiple starts and stops for the same video data session;

multiple retries attempting to send the same video content; and

video data sessions having abnormal behavioral or temporalcharacteristics.

Other factors associated with the video transactions can be use toderive user QoE. For example, by analyzing video transactions, themonitoring system can determine an original video bit rate, a transcodedbit rate, an effective throughput for video sessions, the number offailed or resent packets, and the video-session duration. Additionalinformation can also be used to determine the user QoE. For example,spikes, halts, and buffering of video data can be used to determine atemporal throughput distribution. Also, retries wherein the userrestarts the stream over and over, aborts in which the user stops thevideo session very quickly, multiple starts and stops within onesession, and other video stream characteristics can be used to evaluateQoE.

The system for evaluating user QoE may further include a videotranscoding device that is under control of a network policy managemententity. The video transcoding device may be coupled to the networkinterfaces and adapted to modify a data rate for the video data. Thevideo transcoding device may also be adapted to insert summary data intovideo data packets. The summary data can be inserted into the video datapackets as a header extension. The summary data may include dataassociated with original and adapted video data, including a codec,bit-rate, and resolution for original data received at the videotranscoding device from a video source, and a codec, bit-rate, andresolution of transcoded video data that is sent from the videotranscoding device to a user.

The processor in the network monitoring system also monitors thetranscoding summary data in the video data packets and determines animpact of video transcoding on the QoE score for the one or more users.The processor sends feedback to the network policy management entity.The feedback indicating the impact of the video transcoding.

In an alternative embodiment, a system for monitoring effects of videotranscoding on user QoE in a wireless network comprises a videotranscoding device adapted to modify a data rate for video data packetsbeing sent through the wireless network to wireless network subscribers.The video transcoding device is further adapted to insert summary datainto video data packets. A network policy management entity is adaptedto enforce policies associated with the wireless network. The networkpolicy management entity is coupled to the video transcoding device andprovides information regarding transcoding of the video data packets.Monitoring probes are coupled to one or more network interfaces. Themonitoring probes are adapted to capture video data packets from thenetwork interfaces. A processor is adapted to analyze the capturedpackets.

Monitoring probes may be located before and after a transcoding deviceso that original, unmodified (i.e. not transcoded) video packets can becaptured before the transcoding device. Additionally, transcoded videopackets are captured from one of the links between the transcodingdevice and the user equipment. The transcoded video packets may becaptured immediately after the transcoding device, or from a link closerto the user equipment. The processor determines a first QoE score and asecond QoE score for the one or more subscribers. The first QoE score isassociated with video data that has not been transcoded in the videotranscoding device, and the second QoE score is associated with videodata that has been transcoded in the video transcoding device. Theprocessor identifies the impact of video transcoding on the network QoEby comparing the first and second QoE scores. The processor thengenerates feedback messages to the network policy management entity. Thefeedback messages comprise information associated with the impact onnetwork QoE due to video transcoding.

The network policy management entity is adapted to enforce the policiesby controlling video transcoding parameters implemented by the videotranscoding device. The network policy management may enforce thepolicies by limiting the video transcoding parameters that the videotranscoding device is allowed to adjust.

The summary data inserted by the video transcoding device is associatedwith original and adapted video data, such as a codec, bit-rate, orresolution for original data received at the video transcoding devicefrom a video source or for transcoded video data that is sent from thevideo transcoding device to subscribers. The processor may furtherassociate the summary data with the first QoE score and the second QoEscore to identify the impact on network QoE due to video transcoding.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, wherein:

FIG. 1 is a high-level block diagram illustrating the components of aUniversal Mobile Telecommunications System (UMTS) 3GT network;

FIG. 2 is a block diagram illustrating the LTE (Long Term Evolution)/SAE(System Architecture Evolution) 4G network architecture; and

FIG. 3 is a flowchart illustrating a process for determining QoEaccording to one embodiment; and

FIG. 4 is a flowchart illustrating a process for using video transcodingdata and QoE feedback according to one embodiment.

DETAILED DESCRIPTION

The invention now will be described more fully with reference to theaccompanying drawings. This invention may, however, be embodied in manydifferent forms and should not be construed as limited to theembodiments set forth herein. Rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art. Oneskilled in the art may be able to use the various embodiments of theinvention.

As mobile data networks experience unprecedented growth in total networktraffic, mobile network operators face unique challenges. Serviceproviders need to maintain sufficiently high quality of service (QoS)levels to minimize churn and maximize service revenue. Historically thiswas a relatively simple problem to solve for networks that were focusedprimarily on mobile voice. With the advent and explosion of mobile videotraffic, it has become more difficult to maintain high QoS levels.

Typical techniques for determining video QoS rely on reconstructing thevideo transport streams. These methods are likely to be ineffective inevaluating mobile video QoS, and reconstructing video transport streamswill not work with future delivery methods. Video content transmissionis moving towards delivery over TCP/HTTP where quality is a function ofthe effective data throughput and the client buffer size. By definition,TCP/HTTP guarantees reliable delivery of the content. Also, videocontent reconstruction is an expensive operation and would not scalewell with the projected growth in mobile video in a cost effective way.In one embodiment of the system described herein, a monitoring systemderives video Quality of Experience (QoE) without reconstructing thevideo stream.

FIG. 1 is a high-level block diagram illustrating the components of aUniversal Mobile Telecommunications System (UMTS) 3G network, which mayinclude UTRAN (Universal Terrestrial Radio Access Network) and GERAN(GSM EDGE Radio Access Network) elements. A plurality of NodeB networkelements 101 serve subscribers in respective cells 102 and are connectedto RNC 103 via an Iub interface. The RNC 103 is coupled to SGSN 104 viaan Iu-PS interface and to MSC 105 via an Iu-CS interface. SGSN 104 iscoupled via a Gn interface to GGSN 106, which provides access toInternet 107. User equipment (UE) 116 within a cell 102 communicateswith the respective NodeB 101.

A monitoring system, including, for example, probes 108 and monitoringsystem controller 109 are coupled to the Iub and/or the Iu interfaces.Probes 108 collect PDUs and session data from the interfaces, such asRRC and NBAP messages from the Iub interfaces and ALCAP and RANAPmessages from Iu interfaces. A service provider or network operator mayaccess data from monitoring system 109 via user interface station 110.Monitoring system 109 may further comprise internal or external memory111 for storing captured data packets, user session data, call recordsconfiguration information, and software application instructions.

The monitoring system may be located in one location, such as a serveror equipment rack in which probes 108 a-c run on separate blades.Alternatively, probes 108 a-c may be located near RNC 103 or SGSN 104and remote from monitoring system controller 109. Probes 108 a-c andmonitoring system controller 109 comprises one or more processorsrunning one or more software applications.

FIG. 2 is a block diagram illustrating the LTE (Long Term Evolution)/SAE(System Architecture Evolution) 4G network architecture. The LTE/SAEnetwork technology represents mobile network evolution to providehigh-rate IP-based services. The standardization entity in charge ofspecifying the mobile standards, which is known as the 3^(rd) GenerationPartnership Project (3GPP), has defined standards for mobiletelecommunication systems, including both the radio access and the corenetwork evolution. The standard is named Evolved Packet System (EPS),and it specifies the evolution of the UTRAN access network—the evolvedUTRAN (eUTRAN) 201—and the concurrent evolution of the Core network—theEvolved Packet Core (EPC) 202. LTE and SAE are commonly used synonymsfor eUTRAN 201 and EPC 202, respectively.

The network comprises a number of different types of network nodes andinterfaces. The nodes include, for example, enhanced NodeBs (eNodeB oreNb) 203 that services subscribers in cells 204, Mobility ManagementEntity (MME) 205, Serving Gateway (S-GW) 206, and Packet Data NetworkGateway (PDN-GW) 207. The interfaces between the nodes in the EPC domainare generally named “S#.” The “X2” interface (between eNodeBs) and “Uu”interface (air interface between eNodeBs 203 and User Equipment 208) arein the eUTRAN domain.

The goal of the EPS technology is to significantly enhance the bandwidthavailable to users and, at the same time, improve the Quality of Service(QoS) of the radio connection. The following nodes operate within theeUTRAN domain. User Equipment (UE) 208 is the subscriber endpoint of theend-to-end services. UE 208 communicates over the Uu interface toeNodeBs 203 on the radio path. eNodeB 203 manages the radio path to UE208 and hosts the physical radio establishment, radio link control, andmedium access control functions. eNodeB 203 also encrypts and decryptsdata toward the radio path and handles the radio resource admission andmanagement.

The following nodes operate within the EPC domain. MME 205 is the noderesponsible for managing the non access stratum (NAS) control planemessages from/to the UE 208. In addition, MME 205 plays a role inselecting S-GW 206 for user plane traffic, coordinates handover inLTE/SAE, and establishes the necessary authentication and securityprocedures. MME 205 also coordinates the bearer assignment to the UE208. S-GW 206 is the endpoint of user plane connections from eNodeBnodes 203. S-GW 106 is an anchor for user plane connections in case ofUE handover between eNodeBs 203. PDN-GW (207) is the network node thatprovides an interface between the EPC with external PDN networks, suchas the Internet 209.

In a complex system such as an LTE/SAE network, the tasks of measuringnetwork performance, troubleshooting network operation, and controllingnetwork service behavior can be very difficult for the network operator.Evolution of the network, such as the introduction and deployment of newnetwork technology, causes additional instability and further problemsin network measurement, troubleshooting and control. In order to performthese tasks, network operators often make use of external monitoringsystems, such as monitoring system 109 (FIG. 1). These monitoringsystems are typically connected to the network in a non-intrusive modethat allows them to sniff data from the network interfaces, processingthe data and provide measurements and reports that help the networkoperator to manage its network. The monitoring system typically needs totrack the UEs' activities in order to provide detailed analysis of theservices used by the subscribers and to collect information about thenetwork's behavior for troubleshooting and optimization purposes.

A monitoring system 210 may be coupled to links in the LTE/SAE networkvia one or more probes 216 to passively monitor and collect signalingdata from one or more interfaces in the network. Monitoring system 210may collect user plane and control plane data from the EPC and eUTRANinterfaces, including, for example, the S1-MME, S10, and S11 interfacesthat have an MME 205 as an endpoint and S1-MME and X2 interfaces thathave an eNodeB 203 as an endpoint. It will be understood that some orall of the other interfaces or links in the network may also bemonitored by monitoring system 210. The monitoring system 210 maycomprise, in one embodiment, one or more processors running one or moresoftware applications that collect, correlate and analyze Protocol DataUnits (PDU) and data packets from eUTRAN 201 and EPC 202.

A service provider or network operator may access data from monitoringsystem 210 via user interface station 211. Monitoring system 210 mayfurther comprise internal or external memory 212 for storing captureddata packets, user session data, call records configuration information,and software application instructions.

The monitoring systems 108-111 (FIG. 1) and 210-212 (FIG. 2) mayincorporate protocol analyzer, session analyzer, and/or traffic analyzerfunctionality that provides OSI (Open Systems Interconnection) layer 2to layer 7 troubleshooting by characterizing IP traffic by links, nodes,applications and servers on the network. Such functionality is provided,for example, by the GeoProbe G10 platform, including the Iris AnalyzerToolset applications and Splprobes, from Tektronix Incorporated. It willbe understood that the monitoring systems illustrated in FIGS. 1 and 2are simplified and that any number of interconnected monitoring systemprobes may be coupled to one or more interfaces within the networks. Asingle monitoring probe may capture data from a particular interface, ortwo or more probes may be coupled to one interface.

The monitoring systems may be coupled to network interfaces via packetcapture devices, such as high-speed, high-density probes that areoptimized to handle high bandwidth IP traffic. The monitoring systempassively captures message traffic from the interfaces withoutinterrupting the network's operation. The monitoring system may captureand correlate the packets associated with specific data sessions onnetwork interfaces. The related packets can be combined into a recordfor a particular flow, session or call on the network. In an alternativeembodiment, the monitoring system may be an active component, such as asoftware agent, that resides on an MME or RNC, for example, and thatcaptures data packets passing into or out of the node.

Streaming video that originates from prerecorded video files or fromlive video feeds is very popular with subscribers on 3G and 4G wirelessnetworks. The video stream typically originates at a source outside themobile network and often must be accessed via the Internet (107, 209).For example, a wireless subscriber (e.g. UE 108 or 208) may establish adata session with a remote video server (115, 215). In a 3G network(FIG. 1), the data session is created through RNC 103, SGSN 104 and GGSN106 to Internet 107 and then to the video source 115. In a 4G network(FIG. 2), the data session is created through MME 205, S-GW 206, andPDN-GW 207 to Internet 209 and again to the video source 215. Thewireless subscriber selects stored video files or live video feeds fromthe video source (115, 215), such as via a webpage hosted on a server.

The video server begins sending video data packets for the selectedvideo through the Internet and across the 3G or 4G network to thesubscriber. The video packets comprise video information that has beencompressed using a selected video compression protocol. The rate atwhich the video information may be transmitted through the 3G or 4Gnetworks is determined by the current capability of the network links.If the network is experiencing a high traffic load, the network may nothave sufficient bandwidth to establish the video connection. In theevent that the session is established between the UE and the videosource, the video packets may be delayed.

The network monitoring system disclosed herein derives video Quality ofExperience (QoE) information without needing to reconstruct the originalvideo stream. The network monitoring system detects video deliverytransactions and identifies the key parameters related to the video tobe delivered, such as the video bit rate, occurrence of resent or failedvideo packets, and the duration of the video session. The networkmonitoring system also tracks the behavioral and temporalcharacteristics of the video content delivery, such as packet deliveryby time. The network monitoring system then determines the effectivethroughput per video transaction or session.

A QoE evaluation process running on the network monitoring systemmaintains a record of video transactions for some or all users as wellas a transactional record of any video delivery. The QoE evaluationsystem identifies the following scenarios and characterizes theassociated session as having a low or poor QoE:

video data that is delivered at less than a target bit rate;

video data sessions that are aborted after short playback;

multiple starts and stops for the same video data session;

multiple retries attempting to send the same video content; and

video data sessions having abnormal behavioral or temporalcharacteristics.

Other factors associated with the video transactions can also be use toderive user QoE. For example, by analyzing video transactions, themonitoring system can determine an original video bit rate, a transcodedbit rate, an effective throughput for video sessions, the number offailed or resent packets, and the video-session duration. Additionalinformation can also be used to determine the user QoE. For example,spikes, halts, and buffering of video data can be used to determine atemporal throughput distribution. Also, retries wherein the userrestarts the stream over and over, aborts in which the user stops thevideo session very quickly, multiple starts and stops within onesession, and other video stream characteristics can be used to evaluateQoE.

It will be understood that other parameters may also be used tocharacterize or evaluate the video session. Moreover, each of theparameters may be assigned the same or different weights for evaluatingthe overall QoE. For example, occurrences where video data is deliveredat less than a target bit rate may have less effect on QoE, or mayreduce a QoE rating less, than video data sessions that are aborted.

In one embodiment, the monitoring system sorts the video data sessioninformation by subscriber, and the QoE for each video transaction may beevaluated separately. The monitoring system may assign a maximum QoEscore to each video data session when the video data session isinitiated. The monitoring system then decrements the QoE score each timecertain parameters are observed. For example, the monitoring system mayassign a maximum QoE score when a subscriber request for a particularvideo is detected. As the video data is being provided to thesubscriber, the monitoring system identifies whether particularpre-determined events occur. These events may include, for example,failure of an actual video bit rate to maintain a minimum level, failureof an entire video file to transfer to the subscriber, delays or breaksin video data transmission that exceed a predetermined duration,multiple transmissions of the same video data packets, and video datathat is transmitted to the subscriber out of order. When these and otherevents are observed, the QoE score for the video session is reduced.

In other embodiments, the QoE scores for multiple video sessions may becombined by straight averaging or by weighted averaging, for example.Weighted averages may assign certain users or certain events a higherweight. Multiple video sessions for a single user or for a group may becombined. A QoE score may be determined for a group of users based onhow successful and/or timely the video data was transmitted to thesubscribers in the group. Multiple video sessions may be evaluated foreach member of the group. In further embodiments, the QoE derivation maybe enhanced using fuzzy logic or other machine learning technique toimprove overall accuracy.

FIG. 3 is a flowchart illustrating a process for determining QoEaccording to one embodiment. In step 301, data packets are captured frominterfaces in a wireless network. The data packets may be captured by anetwork monitoring system, for example. In step 302, video sessions aredetected within the captured video packets. The video deliverytransactions within the video sessions are monitored in step 303. Thenetwork monitoring system or other QoE measurement system identifies keyparameters related to the video sessions in step 304.

A video bit rate is determined for each of the video sessions in step305, and occurrences of resent or failed video packets are detected instep 306. In step 307, a video-session duration is determined. Aneffective throughput per video transaction or session is then determinedin step 308. Finally, in step 309, a quality of experience (QoE) isdetermined for some or all users associated with the video transactions.

The QoE may be determined based upon detection of video data that isdelivered at less than a target bit rate, video data sessions that areaborted after partial playback, multiple starts and stops for a singlevideo data session, multiple retries attempting to send the same videocontent, and video data sessions having abnormal behavioral or temporalcharacteristics.

Service providers may deploy transcoding-based optimization solutionswithin wireless networks to handle the impact of the explosive growth ofmobile video demand. On embodiment of a transcoding-based optimizationsolution is described and disclosed in pending U.S. patent applicationSer. No. 12/980,199, titled “Adaptive Control of Video Transcoding inMobile Networks,” which was filed Dec. 28, 2010, the disclosure of whichis incorporated herein by reference in its entirety.

For example, the data rate for video data being transmitted through awireless network may be adjusted based upon cell congestion levels. Anetwork monitoring system identifies the congestion levels in networkcells based upon data traffic captured from network interfaces. When acell congestion level reaches a first level, an alert is sent to a videotranscoding device. The video transcoding device adjusts the data ratefor video data being sent to one or more subscribers in the congestedcell. The data rate adjustments may be based upon a subscriber profileor a user equipment type. When cell congestion levels drop below asecond threshold, the monitoring system sends a second alert indicatingthat the video data rate can be increased.

In some situations, may be the video packets may not reach thesubscriber at a sufficient rate for the UE to accurately display theselected video. The UE may display video that freezes while waiting forthe next video data. Subscribers usually find this type of videodifficult to watch and the result is a low Quality of Experience (QoE)for video services on the network. For example, if a selected videorequires 800 kpbs, but the mobile network only has 500 kbps capacityavailable, then the network may not establish the session. If thenetwork does establish a session, the available bandwidth will not alldelivery of the video at 800 kbps, which will result in an extremelypoor experience for the subscriber. At best, the subscriber will see astart/stop playback as the UE continually runs out of buffered data andthen has to refill the buffer.

Video transcoding may be used to optimize video delivery over mobile 3Gand 4G networks. Typically, transcoding devices are designed totranscode video content to a lower bit rate. For example, video packetsthat are originally transmitted at 800 kbps can be re-encoded by thetranscoder to 400-500 kbps with very little degradation inuser-perceived quality. Transcoding can also be applied to reduce screenresolution where appropriate. Most subscriber equipment, such as mobilephones and PDAs, has a small display screen. Images usually can bedisplayed at a lower resolution on these small screens withoutsignificant loss of user enjoyment. The video data may be reduced byreducing the screen resolution, which may result in a lower overall datarate that can be supported by the network.

The video data is transcoded to a lower bit rate prior to entering themobile network or at the edge of the network. Then the transcoded,lower-rate data is sent to the subscriber. These bit-rate reductionscorrespond to direct savings in network utilization which provides twosignificant benefits to mobile operators: reduced capex/opex (the samecontent can be delivered with less infrastructure) and improved QoE(optimizing the bandwidth enables more users to have good QoE).

While the use of video transcoding is effective, there is no currentsolution to use transcoding in an adaptive manner based upon real-timeknowledge of the mobile network's conditions. Instead, current solutionsassume a certain level of available bandwidth and then reduce all videodata rates without regard to actual network conditions. Without feedbackor network condition information, the transcoding process has to beconfigured in a static manner. The operator may at best designatedifferent transcoding settings by time of day. Additionally, the videotranscoding systems have no knowledge of, or feedback regarding, thenetwork resources that are impacted by a particular optimizationdecision. Using a network intelligence system, such as the networkmonitoring systems described above, real-time data is available that canbe used to select a transcoding rate in a way that optimizes QoE andresource usage based on what is actually occurring in the network.

In a 3G network, such as illustrated in FIG. 1, transcoding may beperformed before or after GGSN 106 at location 112 or 113. A PolicyDecision Point (PDP)/Policy Enforcement Point (PEP) 114 may controltranscoding 112, 113 based upon information from the monitoring system.For example, when monitoring system 109 identifies cell congestion incell 102 a or 102 b, the monitoring system 109 notifies PDP/PEP 114 ofthe congestion level. PDP/PEP 114 then directs transcoding 112, 113 touse a lower video data rate for packets addressed to UE in the congestedcell or cells. In other embodiments, the monitoring system identifiescell congestion limitations directly to the transcoding equipment 112,113 without using PDP/PEP 114. Similarly, in a 4G network, asillustrated in FIG. 2, the transcoding may be performed by element 213between PDN-GW 207 and Internet 209. PDP/PEP 214 may control transcoding213 using information from monitoring system 210. The monitoring systemmay communicate with other network policy management entities, such as aPolicy Enforcement Point (PEP), a Policy Decision Point (PDP), a PolicyCharging and Control (PCC) function, a Policy and Charging RulesFunction (PCRF), or a Policy and Charging Execution Function (PCEF), tocontrol transcoding in the network.

Embodiments of the monitoring system also support other adaptivetranscoding scenarios. The monitoring system may classify video trafficinto customer segments, such as by identifying high-value ornon-high-value subscribers or certain equipment types, and then applydifferent transcoding schemes to enhance QoE for desired segments. Themonitoring system may also monitor radio key performance indicators(KPIs), such as interference levels, and apply different transcodingschemes to improve QoE issues caused by radio impairment.

A service provider may establish policies that control how transcodingis handled within the network. The benefits of transcoding can bemaximized using the monitoring system information. In the presence ofnetwork congestion, more users can continue to receive video by usingmore aggressive transcoding optimization. In the absence of networkcongestion, user experience is enhanced by using less aggressivetranscoding, thus improving video quality. In any scenario, carriernetwork infrastructure may be reduced for any network with apredominance of mobile video traffic, which is expected for all mobilenetworks in the near future. Reduced infrastructure means immediatecapex avoidance and ongoing opex savings.

Using the systems and methods described and disclosed above, it ispossible to efficiently derive QoE for mobile video using a networkmonitoring system. However, there is currently no mechanism to ensurethat video transcoding does not have a negative impact on userexperience. Additional embodiments of the network monitoring systems andmethods described herein correlate QoE data back to policy decisionsthat are made or enforced at the video transcoder to ascertain whetherthe selected video transcoding had a desired effect on user experience.Further, the network monitoring system determines when the feedback fedto the video transcoder causes too aggressive optimization beingperformed. Thus, the mobile operator ensures closed-loop feedback formobile video optimization to ensure the highest total QoE possible.

In one embodiment, the video transcoder system, such as transcoder 112,113, or 213, inserts a header extension or equivalent data into controlpackets associated with the video stream. For example, header extensioninformation may be inserted into HTTP header or RTSP control packetsthat are associated with establishing a video session. The headerextension is labeled “x-video-transcoder:” in one embodiment. The headerextension carries the original codec/bit-rate/resolution and the adaptedor transcoded codec/bit-rate/resolution. The header extension is ignoredby the end user's equipment, such as UE 108 or 208. However, the networkmonitoring system monitors the transcoder extension in the video datapackets. The header extension or equivalent data may comprise summaryinformation related to a transcoding process, such as data associatedwith original and adapted video data, such as a codec, bit-rate, orresolution for original data received at a video transcoding device froma video source, and a codec, bit-rate, or resolution of transcoded videodata that is sent from the video transcoding device to subscribers.

The network monitoring system incorporates knowledge of the selectedtranscoding directly into network troubleshooting tools, such as asession analysis or a protocol analysis. The network monitoring systemfurther provides network-wide aggregate views of the impact of videotranscoding on QoE. An analysis engine in the network monitoring systemidentifies when transcoding causes an abnormal impact on the network.The abnormal impact may be, for example, a poor QoE or a drop in QoEfollowing the start of transcoding. The monitoring system sends updatesto the transcoding policy control engine, such as PDP/PEP 114 or 214,which may in turn further adjust the transcoding rate.

FIG. 4 is a flowchart illustrating a process for using video transcodingdata and QoE feedback according to one embodiment. In step 401, a videotranscoder inserts a header extension or equivalent data into the videodata packets. The header extension may carry data associated withoriginal and adapted video data, such as the codec, bit-rate, andresolution for the original data received at the transcoder from thevideo source, and the codec, bit-rate and resolution of the transcodedvideo data that is sent from the transcoder to the UE. The headerextension would be ignored by the UE, which would not need the extendedheader information to process and display the video data. In step 402, anetwork monitoring system monitors the transcoder extension data in theheader. Using the data in the video header extension, in step 403, thenetwork monitoring system incorporates knowledge of the videotranscoding directly into troubleshooting tools. The network monitoringsystem may generate a session analysis or a protocol analysis based uponthe video data packets.

The network monitoring system provides network-wide, aggregate views ofthe impact of transcoding on QoE in step 404. The network monitoringsystem further identifies any abnormal impact from the transcoding instep 405. An analysis engine or other troubleshooting tool may be usedin the network monitoring system to identify a drop in QoE or a poor QoEthat results from the transcoding. In step 406, the network monitoringsystem sends updates to the transcoding policy control engine. Thetranscoding policy engine may adjust the transcoding currently beingimplemented in the network as a result of the feedback from the networkmonitoring system.

Many modifications and other embodiments of the invention will come tomind to one skilled in the art to which this invention pertains havingthe benefit of the teachings presented in the foregoing descriptions,and the associated drawings. Therefore, it is to be understood that theinvention is not to be limited to the specific embodiments disclosed.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

1. A method for evaluating a user Quality of Experience (QoE) in awireless network, comprising: capturing data packets from networkinterfaces in the wireless network; identifying video sessionsassociated with the captured data packets; monitoring the key parametersfor the video sessions within the captured data packets; determining aneffective throughput per video session based upon the key parameters;and calculating a QoE score for one or more users associated with thevideo sessions.
 2. The method of claim 1, wherein the key parameterscomprise one or more of a video bit rate, a number of resent video datapackets, a number of failed video data packets, and a video sessionduration.
 3. The method of claim 1, further comprising: determining avideo bit rate for each of the video sessions; and calculating the QoEscore based at least in part upon the video bit rate for one or morevideo sessions.
 4. The method of claim 1, further comprising: detectingoccurrences of resent or failed video data packets; and calculating theQoE score based at least in part upon a number of resent or failed videodata packets.
 5. The method of claim 1, further comprising: determininga duration of a selected video session; and calculating the QoE scorebased at least in part upon the duration of the selected video session.6. The method of claim 1, further comprising: inserting a headerextension into transcoded video data packets associated with the videosessions; evaluating an impact of video transcoding on the QoE; andsending updates to a transcoding policy engine, the updates identifyingthe impact of video transcoding on QoE.
 7. A system for evaluating userQuality of Experience (QoE) in a wireless network, comprising: aplurality of monitoring probes coupled to one or more networkinterfaces, the monitoring probes adapted to capture data from thenetwork interfaces; and a processor adapted to analyze the data capturedfrom the network interfaces, the processor operating to: identify videodata within the data captured from the network interfaces; identify oneor more video sessions associated with the video data; determine aneffective throughput for the one or more video sessions; and determine aQoE score for one or more users associated with the video sessions, theQoE score based upon the effective throughput of the one or more videosessions.
 8. The system of claim 7, the processor further operating to:identify key parameters within the video data; and determine theeffective throughput for the video sessions based at least in part uponthe key parameters.
 9. The system of claim 7, the processor furtheroperating to: determine video bit rates for the one or more videosessions; and determine the effective throughput for the video sessionsbased at least in part upon the video bit rates.
 10. The system of claim7, the processor further operating to: detect resent or failed videodata packets associated with the one or more video sessions; anddetermine the effective throughput for the video sessions based at leastin part upon the resent or failed video data packets.
 11. The system ofclaim 7, the processor further operating to: detect durations of the oneor more video sessions; and determine the QoE score based at least inpart upon the durations of the video sessions.
 12. The system of claim10, further comprising: a video transcoding device coupled to thenetwork interfaces and under control of a network policy managemententity, the video transcoding device adapted to modify a data rate forthe video data, the video transcoding device further adapted to insertsummary data into video data packets.
 13. The system of claim 12,wherein the summary data is inserted into video data packets as a headerextension.
 14. The system of claim 12, wherein the summary datacomprises: data associated with original and adapted video data,including a codec, bit-rate, and resolution for original data receivedat the video transcoding device from a video source, and a codec,bit-rate, and resolution of transcoded video data that is sent from thevideo transcoding device to a user.
 15. The system of claim 12, theprocessor further operating to: monitor the transcoding summary data inthe video data packets; and determine an impact of video transcoding onthe QoE score for the one or more users.
 16. The system of claim 15, theprocessor further operating to: send feedback to the network policymanagement entity, the feedback indicating the impact of the videotranscoding.
 17. A system for monitoring effects of video transcoding onuser Quality of Experience (QoE) in a wireless network, comprising: avideo transcoding device adapted to modify a data rate for video datapackets being sent through the wireless network to wireless networksubscribers, the video transcoding device further adapted to insertsummary data into video data packets; a network policy management entityadapted to enforce policies associated with the wireless network, thenetwork policy management entity coupled to the video transcoding deviceand providing information regarding transcoding of the video datapackets; a plurality of monitoring probes coupled to one or more networkinterfaces, the monitoring probes adapted to capture video data packetsfrom the network interfaces and further comprising a processor adaptedto analyze the captured packets, the processor operating to: determine afirst QoE score for one or more subscribers, the first QoE scoreassociated with video data that has not been transcoded in the videotranscoding device; determine a second QoE score for the one or moresubscribers, the second QoE score associated with video data that hasbeen transcoded in the video transcoding device; identify an impact onnetwork QoE due to video transcoding by comparing the first and secondQoE scores; and generate feedback messages to the network policymanagement entity, the feedback messages comprising informationassociated with the impact on network QoE due to video transcoding. 18.The system of claim 17, wherein the network policy management entity isfurther adapted to enforce the policies by controlling video transcodingparameters implemented by the video transcoding device.
 19. The systemof claim 17, wherein the network policy management entity is furtheradapted to enforce the policies by limiting video transcoding parametersthat the video transcoding device is allowed to adjust.
 20. The systemof claim 17, wherein the summary data comprises: data associated withoriginal and adapted video data, including a codec, bit-rate, orresolution for original data received at the video transcoding devicefrom a video source, and a codec, bit-rate, or resolution of transcodedvideo data that is sent from the video transcoding device tosubscribers; and wherein the processor further operating to: associatethe summary data with the first QoE score and the second QoE score toidentify the impact on network QoE due to video transcoding.
 21. Asystem for evaluating user Quality of Experience (QoE) in a wirelessnetwork, comprising: a plurality of monitoring probes coupled to one ormore network interfaces, the monitoring probes adapted to capture datafrom the network interfaces; and a processor adapted to analyze the datacaptured from the network interfaces, the processor operating to:identify one or more video sessions associated with the video data;monitoring transactional and temporal characteristics of the videosessions; and determine a QoE score for one or more users associatedwith the video sessions, the QoE score based upon the transactional andtemporal characteristics of the video sessions.
 22. The system of claim21, wherein the transactional and temporal characteristics comprise oneor more characteristics selected from the group consisting of: anoriginal video bit rate; a transcoded video bit rate; an effectivethroughput for the video session; and a duration of a video session. 23.The system of claim 21, wherein the transactional and temporalcharacteristics comprise one or more characteristics selected from thegroup consisting of: a number of failed video packets; a number ofresent video packets; a number of video session restarts; a number ofvideo session aborts; and a number of starts and stops during a singlevideo session.
 24. The system of claim 21, wherein the transactional andtemporal characteristics comprise a temporal throughput distribution fora video session, the temporal throughput distribution determined basedupon spikes, halts and buffering in a video session.